Unlike most guides I've seen about ML, this one does a good job of focusing on developing and deploying a simple model first, then iterating. There are also lot of practical tips here, especially around feature engineering.
> the second phase of machine learning involves pulling in as many features as possible and combining them in intuitive ways. During this phase, all of the metrics should still be rising
As Google points out, after you build an initial model, the next step to increase accuracy is to perform feature engineering. They explain that this can be done manually or automatically using something like deep learning. Another option that people here might consider is using a library like Featuretools (https://github.com/featuretools/featuretools) for "automated feature engineering". Note: I am one of the developers.
> the second phase of machine learning involves pulling in as many features as possible and combining them in intuitive ways. During this phase, all of the metrics should still be rising
As Google points out, after you build an initial model, the next step to increase accuracy is to perform feature engineering. They explain that this can be done manually or automatically using something like deep learning. Another option that people here might consider is using a library like Featuretools (https://github.com/featuretools/featuretools) for "automated feature engineering". Note: I am one of the developers.
Our goal is to help you increase the performance of your models without sacrificing the interoperability of your features. We have a post up about how our algorithm works here: https://www.featurelabs.com/blog/deep-feature-synthesis/. There are also plenty of real world demos on our website: https://www.featuretools.com/demos